Advanced Lane Finding Project¶
The goals / steps of this project are the following:
- Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
- Apply a distortion correction to raw images.
- Use color transforms, gradients, etc., to create a thresholded binary image.
- Apply a perspective transform to rectify binary image ("birds-eye view").
- Detect lane pixels and fit to find the lane boundary.
- Determine the curvature of the lane and vehicle position with respect to center.
- Warp the detected lane boundaries back onto the original image.
- Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.
Step One. First, I'll compute the camera calibration using chessboard images¶
Note: If you read in an image using matplotlib.image.imread() you will get an RGB image, but if you read it in using OpenCV cv2.imread() this will give you a BGR image
Idea¶
There is also HSV color space (hue, saturation, and value), and HLS space (hue, lightness, and saturation). These are some of the most commonly used color spaces in image analysis.
To get some intuition about these color spaces, you can generally think of Hue as the value that represents color independent of any change in brightness. So if you imagine a basic red paint color, then add some white to it or some black to make that color lighter or darker -- the underlying color remains the same and the hue for all of these colors will be the same.
On the other hand, Lightness and Value represent different ways to measure the relative lightness or darkness of a color. For example, a dark red will have a similar hue but much lower value for lightness than a light red. Saturation also plays a part in this; saturation is a measurement of colorfulness. So, as colors get lighter and closer to white, they have a lower saturation value, whereas colors that are the most intense, like a bright primary color (imagine a bright red, blue, or yellow), have a high saturation value. You can get a better idea of these values by looking at the 3D color spaces pictured below.
In [1]:
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
%matplotlib inline
In [2]:
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*9,3), np.float32)
objp[:,:2] = np.mgrid[0:9,0:6].T.reshape(-1,2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
# Make a list of calibration images
images = glob.glob('../camera_cal/calibration*.jpg')
print (images)
# Step through the list and search for chessboard corners
for fname in images:
img = cv2.imread(fname)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (9,6),None)
print (ret)
# If found, add object points, image points
if ret == True:
objpoints.append(objp)
imgpoints.append(corners)
# Draw and display the corners
img = cv2.drawChessboardCorners(img, (9,6), corners, ret)
#cv2.imshow('img',img)
#cv2.waitKey(5000)
#cv2.destroyAllWindows()
Step Two. Apply a distortion correction to raw images.¶
In [3]:
def calibrate_camera(objpoints, imgpoints, img):
img_size = (img.shape[1], img.shape[0])
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size,None,None)
return ret, mtx, dist, rvecs, tvecs
In [4]:
# TODO: Write a function that takes an image, object points, and image points
# performs the camera calibration, image distortion correction and
# returns the undistorted image
def cal_undistort(img, objpoints, imgpoints):
# Use cv2.calibrateCamera() and cv2.undistort()
# img_size = (img.shape[1], img.shape[0])
ret, mtx, dist, rvecs, tvecs = calibrate_camera(objpoints, imgpoints, img)
undist = cv2.undistort(img, mtx, dist, None, mtx)
return undist
Example of the calibration1.jpg - Original and Undistorted Image¶
In [5]:
%matplotlib inline
image2 = images[0]
img = cv2.imread(image2)
undistorted = cal_undistort(img, objpoints, imgpoints)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(undistorted)
ax2.set_title('Undistorted Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
Pipeline¶
Step Three. Use color transforms, gradients, etc., to create a thresholded binary image.¶
Step Image Thresholding¶
In [6]:
# Define a function that takes an image, gradient orientation,
# and threshold min / max values.
def abs_sobel_thresh(img, orient='x', sobel_kernel=3, thresh=(0,255)):
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
#gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Apply x or y gradient with the OpenCV Sobel() function
# and take the absolute value
if orient == 'x':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel))
if orient == 'y':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel))
# Rescale back to 8 bit integer
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
# Create a copy and apply the threshold
binary_output = np.zeros_like(scaled_sobel)
# Here I'm using inclusive (>=, <=) thresholds, but exclusive is ok too
binary_output[(scaled_sobel >= thresh[0]) & (scaled_sobel <= thresh[1])] = 1
# Return the result
return binary_output
In [7]:
# Choose a Sobel kernel size
ksize = 3 # Choose a larger odd number to smooth gradient measurements
testOneImage = mpimg.imread('../test_images/test1.jpg')
# Run the function
grad_binary = abs_sobel_thresh(testOneImage, orient='x', sobel_kernel=ksize ,thresh=(30, 100))
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(testOneImage)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(grad_binary, cmap='gray')
ax2.set_title('Thresholded Gradient', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
Magnitude of the Gradient¶
In [8]:
# Define a function to return the magnitude of the gradient
# for a given sobel kernel size and threshold values
def mag_thresh(img, sobel_kernel=3, mag_thresh=(0, 255)):
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Take both Sobel x and y gradients
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# Calculate the gradient magnitude
gradmag = np.sqrt(sobelx**2 + sobely**2)
# Rescale to 8 bit
scale_factor = np.max(gradmag)/255
gradmag = (gradmag/scale_factor).astype(np.uint8)
# Create a binary image of ones where threshold is met, zeros otherwise
binary_output = np.zeros_like(gradmag)
binary_output[(gradmag >= mag_thresh[0]) & (gradmag <= mag_thresh[1])] = 1
# Return the binary image
return binary_output
In [9]:
# Run the function
mag_binary = mag_thresh(testOneImage, sobel_kernel=3, mag_thresh=(30, 100))
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(testOneImage)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(mag_binary, cmap='gray')
ax2.set_title('Thresholded Magnitude', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
Direction of the Gradient¶
In [10]:
# Define a function to threshold an image for a given range and Sobel kernel
def dir_threshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):
# Grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Calculate the x and y gradients
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# Take the absolute value of the gradient direction,
# apply a threshold, and create a binary image result
absgraddir = np.arctan2(np.absolute(sobely), np.absolute(sobelx))
binary_output = np.zeros_like(absgraddir)
binary_output[(absgraddir >= thresh[0]) & (absgraddir <= thresh[1])] = 1
# Return the binary image
return binary_output
In [11]:
# Run the function
dir_binary = dir_threshold(testOneImage, sobel_kernel=15, thresh=(0.7, 1.3))
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(testOneImage)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(dir_binary, cmap='gray')
ax2.set_title('Thresholded Grad. Dir.', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [12]:
# Apply each of the thresholding functions
gradx = abs_sobel_thresh(testOneImage, orient='x', sobel_kernel=ksize, thresh=(20, 255))
grady = abs_sobel_thresh(testOneImage, orient='y', sobel_kernel=ksize, thresh=(20, 255))
mag_binary = mag_thresh(testOneImage, sobel_kernel=ksize, mag_thresh=(20, 255))
dir_binary = dir_threshold(testOneImage, sobel_kernel=ksize, thresh=(0, np.pi/2))
In [13]:
combined = np.zeros_like(dir_binary)
combined[((gradx == 1) & (grady == 1)) | ((mag_binary == 1) & (dir_binary == 1))] = 1
print (combined)
In [14]:
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(testOneImage)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(combined, cmap='gray')
ax2.set_title('Thresholded Grad. Dir.', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
Color Spaces Transform¶
Experimenting with RGB¶
In [15]:
testOneImage = mpimg.imread('../test_images/test1.jpg')
In [16]:
thresh = (200, 255)
gray = cv2.cvtColor(testOneImage, cv2.COLOR_RGB2GRAY)
binary = np.zeros_like(gray)
binary[(gray > thresh[0]) & (gray <= thresh[1])] = 1
In [17]:
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(gray, cmap='gray')
ax1.set_title('Gray', fontsize=50)
ax2.imshow(binary, cmap='gray')
ax2.set_title('Gray Binary', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [18]:
R = testOneImage[:,:,0]
G = testOneImage[:,:,1]
B = testOneImage[:,:,2]
In [19]:
thresh = (220, 255)
binary = np.zeros_like(R)
binary[(R > thresh[0]) & (R <= thresh[1])] = 1
In [20]:
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(gray, cmap='gray')
ax1.set_title('R Channel', fontsize=50)
ax2.imshow(binary, cmap='gray')
ax2.set_title('R Binary', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
Experimenting with HLS¶
In [21]:
hls = cv2.cvtColor(testOneImage, cv2.COLOR_RGB2HLS)
H = hls[:,:,0]
L = hls[:,:,1]
S = hls[:,:,2]
Experimenting on S (Saturation)¶
In [22]:
thresh = (90, 255)
s_binary = np.zeros_like(S)
s_binary[(S > thresh[0]) & (S <= thresh[1])] = 1
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(hls)
ax1.set_title('S Channel', fontsize=50)
ax2.imshow(s_binary, cmap='gray')
ax2.set_title('S Binary', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
Experimenting on H (Hue)¶
In [23]:
thresh = (20, 100)
h_binary = np.zeros_like(H)
h_binary[(H > thresh[0]) & (H <= thresh[1])] = 1
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(hls)
ax1.set_title('H Channel', fontsize=50)
ax2.imshow(h_binary, cmap='gray')
ax2.set_title('H Binary', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
Experimenting on L (Lightness)¶
In [24]:
thresh = (200, 255)
L_binary = np.zeros_like(L)
L_binary[(L > thresh[0]) & (L <= thresh[1])] = 1
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(hls)
ax1.set_title('L Channel', fontsize=50)
ax2.imshow(L_binary, cmap='gray')
ax2.set_title('L Binary', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
Combining Color and Gradients¶
In [25]:
# Edit this function to create your own pipeline.
def pipeline(img, s_thresh=(170, 255), sx_thresh=(20, 100)):
img = np.copy(img)
# Convert to HSV color space and separate the V channel
hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HLS).astype(np.float)
l_channel = hsv[:,:,1]
s_channel = hsv[:,:,2]
# Sobel x
sobelx = cv2.Sobel(l_channel, cv2.CV_64F, 1, 0) # Take the derivative in x
abs_sobelx = np.absolute(sobelx) # Absolute x derivative to accentuate lines away from horizontal
scaled_sobel = np.uint8(255*abs_sobelx/np.max(abs_sobelx))
# Threshold x gradient
sxbinary = np.zeros_like(scaled_sobel)
sxbinary[(scaled_sobel >= sx_thresh[0]) & (scaled_sobel <= sx_thresh[1])] = 1
# Threshold color channel
s_binary = np.zeros_like(s_channel)
s_binary[(s_channel >= s_thresh[0]) & (s_channel <= s_thresh[1])] = 1
# Stack each channel
# Note color_binary[:, :, 0] is all 0s, effectively an all black image. It might
# be beneficial to replace this channel with something else.
color_binary = np.dstack(( np.zeros_like(sxbinary), sxbinary, s_binary))
# Combine the two binary thresholds
combined_binary = np.zeros_like(sxbinary)
combined_binary[(s_binary == 1) | (sxbinary == 1)] = 1
return color_binary, combined_binary
result_color_binary, result_combined_binary = pipeline(testOneImage)
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(result_color_binary)
ax1.set_title('Stacked thresholds', fontsize=40)
ax2.imshow(result_combined_binary, cmap='gray')
ax2.set_title('Combined S channel and gradient thresholds', fontsize=40)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
Step Four. Apply a perspective transform to rectify binary image ("birds-eye view").¶
Steps we’ve covered so far:
- Camera calibration
- Distortion correction
- Color/gradient threshold
- Perspective transform
After doing these steps, you’ll be given two additional steps for the project:
- Detect lane lines
- Determine the lane curvature
In [26]:
%matplotlib inline
image7 = cv2.imread(images[6])
plt.figure(figsize=(10, 6), dpi=80)
plt.suptitle('Original Image', fontsize=20)
plt.plot(1200,330,'.') # top right
plt.plot(1190,550,'.') # bottom right
plt.plot(920,300,'.') # top left
plt.plot(920,590,'.') # bottom left
plt.imshow(image7)
Out[26]:
In [56]:
def birds_eye(img, mtx, dist):
""" Birds eye first undistorts the image, using the calibration from earlier.
Next, using defined source image points and destination points,
it will transform the image as if the road was viewed from above,
like a bird would see. Returns the birds eye image and transform matrix.
"""
# Put the image through the pipeline to get the binary image
result_color_binary, result_combined_binary = pipeline(img)
# Undistort
undist = cv2.undistort(result_combined_binary, mtx, dist, None, mtx)
# undist = cv2.undistort(img, mtx, dist, None, mtx)
# Grab the image shape
img_size = (undist.shape[1], undist.shape[0])
# Source points - defined area of lane line edges
src = np.float32([[690,450],[1110,img_size[1]],[175,img_size[1]],[595,450]])
# 4 destination points to transfer
offset = 300 # offset for dst points
dst = np.float32([[img_size[0]-offset, 0],[img_size[0]-offset, img_size[1]],
[offset, img_size[1]],[offset, 0]])
# Use cv2.getPerspectiveTransform() to get M, the transform matrix
M = cv2.getPerspectiveTransform(src, dst)
# Use cv2.warpPerspective() to warp the image to a top-down view
top_down = cv2.warpPerspective(undist, M, img_size)
return top_down, M
ret, mtx, dist, rvecs, tvecs = calibrate_camera(objpoints, imgpoints, testOneImage)
binary_warped, perspective_M = birds_eye(testOneImage, mtx, dist)
In [57]:
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(testOneImage)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(binary_warped, cmap='gray')
ax2.set_title('Undistorted and Warped Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
Step Five. Detect lane pixels and fit to find the lane boundary.¶
In [30]:
histogram = np.sum(binary_warped[img.shape[0]/2:,:], axis=0)
plt.plot(histogram)
Out[30]:
In [31]:
# Assuming you have created a warped binary image called "binary_warped"
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[binary_warped.shape[0]/2:,:], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]/2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# Choose the number of sliding windows
nwindows = 9
# Set height of windows
window_height = np.int(binary_warped.shape[0]/nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated for each window
leftx_current = leftx_base
rightx_current = rightx_base
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2)
cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2)
# Identify the nonzero pixels in x and y within the window
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
In [32]:
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
plt.imshow(out_img)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
Out[32]:
In [33]:
# Assume you now have a new warped binary image
# from the next frame of video (also called "binary_warped")
# It's now much easier to find line pixels!
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
margin = 100
left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
In [34]:
# Create an image to draw on and an image to show the selection window
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin, ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin, ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# Draw the lane onto the warped blank image
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
plt.imshow(result)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
Out[34]:
In [58]:
# window settings
# warped = mpimg.imread('../test_images/test1.jpg')
window_width = 50
window_height = 80 # Break image into 9 vertical layers since image height is 720
margin = 100 # How much to slide left and right for searching
def window_mask(width, height, img_ref, center,level):
output = np.zeros_like(img_ref)
output[int(img_ref.shape[0]-(level+1)*height):int(img_ref.shape[0]-level*height),max(0,int(center-width/2)):min(int(center+width/2),img_ref.shape[1])] = 1
return output
def find_window_centroids(image, window_width, window_height, margin):
window_centroids = [] # Store the (left,right) window centroid positions per level
window = np.ones(window_width) # Create our window template that we will use for convolutions
# First find the two starting positions for the left and right lane by using np.sum to get the vertical image slice
# and then np.convolve the vertical image slice with the window template
# Sum quarter bottom of image to get slice, could use a different ratio
l_sum = np.sum(binary_warped[int(3*binary_warped.shape[0]/4):,:int(binary_warped.shape[1]/2)], axis=0)
l_center = np.argmax(np.convolve(window,l_sum))-window_width/2
r_sum = np.sum(binary_warped[int(3*binary_warped.shape[0]/4):,int(binary_warped.shape[1]/2):], axis=0)
r_center = np.argmax(np.convolve(window,r_sum))-window_width/2+int(binary_warped.shape[1]/2)
# Add what we found for the first layer
window_centroids.append((l_center,r_center))
# Go through each layer looking for max pixel locations
for level in range(1,(int)(binary_warped.shape[0]/window_height)):
# convolve the window into the vertical slice of the image
image_layer = np.sum(binary_warped[int(binary_warped.shape[0]-(level+1)*window_height):int(binary_warped.shape[0]-level*window_height),:], axis=0)
conv_signal = np.convolve(window, image_layer)
# Find the best left centroid by using past left center as a reference
# Use window_width/2 as offset because convolution signal reference is at right side of window, not center of window
offset = window_width/2
l_min_index = int(max(l_center+offset-margin,0))
l_max_index = int(min(l_center+offset+margin,binary_warped.shape[1]))
l_center = np.argmax(conv_signal[l_min_index:l_max_index])+l_min_index-offset
# Find the best right centroid by using past right center as a reference
r_min_index = int(max(r_center+offset-margin,0))
r_max_index = int(min(r_center+offset+margin,binary_warped.shape[1]))
r_center = np.argmax(conv_signal[r_min_index:r_max_index])+r_min_index-offset
# Add what we found for that layer
window_centroids.append((l_center,r_center))
return window_centroids
window_centroids = find_window_centroids(binary_warped, window_width, window_height, margin)
In [59]:
# If we found any window centers
if len(window_centroids) > 0:
# Points used to draw all the left and right windows
l_points = np.zeros_like(binary_warped)
r_points = np.zeros_like(binary_warped)
# Go through each level and draw the windows
for level in range(0,len(window_centroids)):
# Window_mask is a function to draw window areas
l_mask = window_mask(window_width,window_height,binary_warped,window_centroids[level][0],level)
r_mask = window_mask(window_width,window_height,binary_warped,window_centroids[level][1],level)
# Add graphic points from window mask here to total pixels found
l_points[(l_points == 255) | ((l_mask == 1) ) ] = 255
r_points[(r_points == 255) | ((r_mask == 1) ) ] = 255
# Draw the results
template = np.array(r_points+l_points,np.uint8) # add both left and right window pixels together
zero_channel = np.zeros_like(template) # create a zero color channle
template = np.array(cv2.merge((zero_channel,template,zero_channel)),np.uint8) # make window pixels green
warpage = np.array(cv2.merge((binary_warped,binary_warped,binary_warped)),np.uint8) # making the original road pixels 3 color channels
output = cv2.addWeighted(warpage, 1, template, 0.5, 0.0) # overlay the orignal road image with window results
# If no window centers found, just display orginal road image
else:
output = np.array(cv2.merge((binary_warped,binary_warped,binary_warped)),np.uint8)
In [60]:
# Display the final results
plt.imshow(output)
plt.title('window fitting results')
plt.show()
Step Six. Determine the curvature of the lane and vehicle position with respect to center.¶
In [61]:
class Line():
def __init__(self):
# was the line detected in the last iteration?
self.detected = False
# x values of the last n fits of the line
self.recent_xfitted = []
#average x values of the fitted line over the last n iterations
self.bestx = None
#polynomial coefficients averaged over the last n iterations
self.best_fit = None
#polynomial coefficients for the most recent fit
self.current_fit = [np.array([False])]
#radius of curvature of the line in some units
self.radius_of_curvature = None
#distance in meters of vehicle center from the line
self.line_base_pos = None
#difference in fit coefficients between last and new fits
self.diffs = np.array([0,0,0], dtype='float')
#x values for detected line pixels
self.allx = None
#y values for detected line pixels
self.ally = None
Step Seven. Warp the detected lane boundaries back onto the original image.¶
In [62]:
# Create an image to draw the lines on
warp_zero = np.zeros_like(binary_warped).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, perspective_M, (testOneImage.shape[1], testOneImage.shape[0]))
Step Eight. Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.¶
In [63]:
# Combine the result with the original image
undistorted = cal_undistort(testOneImage, objpoints, imgpoints)
result = cv2.addWeighted(undistorted, 1, newwarp, 0.3, 0)
plt.imshow(result)
Out[63]:
Test Video¶
In [64]:
# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML
In [65]:
white_output = 'output.mp4'
clip1 = VideoFileClip("../project_video.mp4")
white_clip = clip1.fl_image(birds_eye) #NOTE: this function expects color images!!
%time white_clip.write_videofile(white_output, audio=False)
In [ ]:
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(white_output))
In [ ]: